giotto.md

name	title	tags	links
giotto-tda	Giotto-TDA	lang/python type/persistence type/images complex/cover-mapper complex/rips complex/rips-weighted complex/alpha complex/cech complex/flag complex/cubical repr/landscape repr/kernel repr/image repr/silhouette feat/amplitude feat/entropy feat/polynomial dist/bottleneck dist/wasserstein vis/diagram vis/betti-surface	name url Repository https://github.com/giotto-ai/giotto-tda name url Docs https://giotto-ai.github.io/gtda-docs/0.5.1/library.html

Giotto-TDA is a well-tested suite of computational topology tools, compatible with the scikit-learn API and framework. From the docs:

giotto-tda is a high performance topological machine learning toolbox in Python built on top of scikit-learn and is distributed under the GNU AGPLv3 license. It is part of the Giotto family of open-source projects.

Supported data types include:

• point clouds,
• tabular data,
• time series data
• (directed) graphs,
• images.

Supported filtrations include:

• (weighted/sparse) Vietoris-Rips,
• weak alpha filtration,
• euclidean Čech filtration,
• filtered cubical complexes,
• (un)directed flag complex filtrations.

Persistence diagrams can also be converted into other representations including persistence landscapes, persistence images and Betti curves. In line with the scikit-learn framework, preprocessing, persistent homology and diagram representations can be combined into a single pipeline.

Under the hood, many demanding workloads are implemented in C++, vectorised and parallelised.